Break down an assignment into subtasks with estimated effort, dependencies, and critical path
AI agents call decompose_task to retrieve information from Moodle-MCP without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool retrieves assignment information and performs analytical processing to generate a decomposition plan. It is a read-based analysis tool that does not create, modify, delete, or execute operations. The output is informational guidance (subtasks, estimates, dependencies, critical path) derived from the assignment data.
From the tool's definition Tool performs decomposition analysis of assignments - breaks down existing data into subtasks, estimates, and dependencies. No modification, deletion, execution, or financial operations occur. Pure analysis/transformation of existing assignment data.
Documented attack patterns abuse exactly the kind of access decompose_task gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Moodle-MCP, and nothing reaches the server without passing your rules. This is the rule we recommend for decompose_task:
{
"version": "1",
"default": "deny",
"tools": {
"decompose_task": {}
}
} decompose_task is read-only, so it stays allowed — but everything else on the server is denied unless you say otherwise.
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Break down an assignment into subtasks with estimated effort, dependencies, and critical path. It is categorised as a Read tool in the Moodle-MCP MCP Server, which means it retrieves data without modifying state.
Register the Moodle- MCP server in PolicyLayer and add a rule for decompose_task: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Moodle-MCP. Nothing to install.
decompose_task is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the decompose_task rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the PolicyLayer policy for decompose_task. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
decompose_task is provided by the Moodle- MCP server (loyaniu/moodle-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Moodle-MCP, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
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24 Moodle-MCP tools catalogued and risk-classified — across an index of 43,000+ MCP servers.